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CIFAR10.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "CIFAR10.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "mount_file_id": "https://gist.github.com/NaiveTomcat/ca04d714a0c5203c55f62c429b7589e3#file-cifar10-ipynb", | |
| "authorship_tag": "ABX9TyObQQ1NPLfxSGn7zK4+t8Dd", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "accelerator": "GPU" | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/NaiveTomcat/ca04d714a0c5203c55f62c429b7589e3/cifar10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "daSNCgr6NL3Y", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "!tar -xzf drive/My\\ Drive/cifar-10-python.tar.gz" | |
| ], | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "s30xeG-OgR5a", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "!pip install tensorflow-gpu" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Pvx4Si-AgTAp", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "# -*- coding: utf-8 -*-\n", | |
| "import numpy as np\n", | |
| "import os\n", | |
| "import sys\n", | |
| "import pickle\n", | |
| "\n", | |
| "def __load_batch__(fpath, label_key='labels'):\n", | |
| " \"\"\"Internal utility for parsing CIFAR data.\n", | |
| " # Arguments\n", | |
| " fpath: path the file to parse.\n", | |
| " label_key: key for label data in the retrieve\n", | |
| " dictionary.\n", | |
| " # Returns\n", | |
| " A tuple `(data, labels)`.\n", | |
| " \"\"\"\n", | |
| " with open(fpath, 'rb') as f:\n", | |
| " if sys.version_info < (3,):\n", | |
| " d = pickle.load(f)\n", | |
| " else:\n", | |
| " d = pickle.load(f, encoding='bytes')\n", | |
| " # decode utf8\n", | |
| " d_decoded = {}\n", | |
| " for k, v in d.items():\n", | |
| " d_decoded[k.decode('utf8')] = v\n", | |
| " d = d_decoded\n", | |
| " data = d['data']\n", | |
| " labels = d[label_key]\n", | |
| "\n", | |
| " data = data.reshape(data.shape[0], 3, 32, 32)\n", | |
| " return data, labels\n", | |
| "\n", | |
| "\n", | |
| "def load_data():\n", | |
| " \"\"\"Loads CIFAR10 dataset.\n", | |
| " # Returns\n", | |
| " Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.\n", | |
| " \"\"\"\n", | |
| " dirname = 'cifar-10-batches-py'\n", | |
| " path = './%s' % dirname\n", | |
| "\n", | |
| " num_train_samples = 50000\n", | |
| "\n", | |
| " x_train = np.empty((num_train_samples, 3, 32, 32), dtype='uint8')\n", | |
| " y_train = np.empty((num_train_samples,), dtype='uint8')\n", | |
| "\n", | |
| " for i in range(1, 6):\n", | |
| " fpath = os.path.join(path, 'data_batch_' + str(i))\n", | |
| " (x_train[(i - 1) * 10000: i * 10000, :, :, :],\n", | |
| " y_train[(i - 1) * 10000: i * 10000]) = __load_batch__(fpath)\n", | |
| "\n", | |
| " fpath = os.path.join(path, 'test_batch')\n", | |
| " x_test, y_test = __load_batch__(fpath)\n", | |
| "\n", | |
| " y_train = np.reshape(y_train, (len(y_train), 1))\n", | |
| " y_test = np.reshape(y_test, (len(y_test), 1))\n", | |
| "\n", | |
| " x_train = x_train.transpose(0, 2, 3, 1)\n", | |
| " x_test = x_test.transpose(0, 2, 3, 1)\n", | |
| "\n", | |
| " return (x_train, y_train), (x_test, y_test)\n", | |
| "\n", | |
| "\n", | |
| "def test_data():\n", | |
| " (x_train, y_train), (x_test, y_test) = load_data()\n", | |
| " print(x_train.shape)\n", | |
| " print(x_test.shape)\n", | |
| " print(y_train.shape)\n", | |
| " print(y_test.shape)" | |
| ], | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "LY2eq5RagZSq", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 261 | |
| }, | |
| "outputId": "c941ef21-889a-44f0-de03-6471ec3bf941" | |
| }, | |
| "source": [ | |
| "\n", | |
| "\n", | |
| "import numpy as np\n", | |
| "import tensorflow as tf\n", | |
| "import tensorflow.keras as keras\n", | |
| "from tensorflow.keras import layers\n", | |
| "from tensorflow.keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, add\n", | |
| "from tensorflow.keras.layers import Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D\n", | |
| "from tensorflow.keras.models import Model, load_model, save_model\n", | |
| "from tensorflow.keras.preprocessing import image\n", | |
| " \n", | |
| " \n", | |
| "import tensorflow.keras.backend as K\n", | |
| "\n", | |
| "import os\n", | |
| "import sys\n", | |
| "import pickle\n", | |
| "\n", | |
| "def __load_batch__(fpath, label_key='labels'):\n", | |
| " \"\"\"Internal utility for parsing CIFAR data.\n", | |
| " # Arguments\n", | |
| " fpath: path the file to parse.\n", | |
| " label_key: key for label data in the retrieve\n", | |
| " dictionary.\n", | |
| " # Returns\n", | |
| " A tuple `(data, labels)`.\n", | |
| " \"\"\"\n", | |
| " with open(fpath, 'rb') as f:\n", | |
| " if sys.version_info < (3,):\n", | |
| " d = pickle.load(f)\n", | |
| " else:\n", | |
| " d = pickle.load(f, encoding='bytes')\n", | |
| " # decode utf8\n", | |
| " d_decoded = {}\n", | |
| " for k, v in d.items():\n", | |
| " d_decoded[k.decode('utf8')] = v\n", | |
| " d = d_decoded\n", | |
| " data = d['data']\n", | |
| " labels = d[label_key]\n", | |
| "\n", | |
| " data = data.reshape(data.shape[0], 3, 32, 32)\n", | |
| " return data, labels\n", | |
| "\n", | |
| "\n", | |
| "def load_data():\n", | |
| " \"\"\"Loads CIFAR10 dataset.\n", | |
| " # Returns\n", | |
| " Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.\n", | |
| " \"\"\"\n", | |
| " dirname = 'cifar-10-batches-py'\n", | |
| " path = './%s' % dirname\n", | |
| "\n", | |
| " num_train_samples = 10000\n", | |
| "\n", | |
| " x_train = np.empty((num_train_samples, 3, 32, 32), dtype='uint8')\n", | |
| " y_train = np.empty((num_train_samples,), dtype='uint8')\n", | |
| "\n", | |
| " for i in range(1, 2):\n", | |
| " fpath = os.path.join(path, 'data_batch_' + str(i))\n", | |
| " (x_train[(i - 1) * 10000: i * 10000, :, :, :],\n", | |
| " y_train[(i - 1) * 10000: i * 10000]) = __load_batch__(fpath)\n", | |
| "\n", | |
| " fpath = os.path.join(path, 'test_batch')\n", | |
| " x_test, y_test = __load_batch__(fpath)\n", | |
| "\n", | |
| " y_train = np.reshape(y_train, (len(y_train), 1))\n", | |
| " y_test = np.reshape(y_test, (len(y_test), 1))\n", | |
| "\n", | |
| " x_train = x_train.transpose(0, 2, 3, 1)\n", | |
| " x_test = x_test.transpose(0, 2, 3, 1)\n", | |
| "\n", | |
| " return (x_train, y_train), (x_test, y_test)\n", | |
| "\n", | |
| "\n", | |
| "def test_data():\n", | |
| " (x_train, y_train), (x_test, y_test) = load_data()\n", | |
| " print(x_train.shape)\n", | |
| " print(x_test.shape)\n", | |
| " print(y_train.shape)\n", | |
| " print(y_test.shape)\n", | |
| " \n", | |
| " \n", | |
| "def Conv2D_BN(x, filters, kernel_size, strides=(1, 1), padding='same', name=None):\n", | |
| " if name:\n", | |
| " bn_name = name + '_bn'\n", | |
| " conv_name = name + '_conv'\n", | |
| " else:\n", | |
| " bn_name = None\n", | |
| " conv_name = None\n", | |
| " x = Conv2D(filters, kernel_size, strides=strides, padding=padding, activation='relu', name=conv_name)(x)\n", | |
| " x = BatchNormalization(name=bn_name)(x)\n", | |
| " return x\n", | |
| " \n", | |
| " \n", | |
| "def identity_block(input_tensor, filters, kernel_size, strides=(1, 1), is_conv_shortcuts=False):\n", | |
| " \"\"\"\n", | |
| " \n", | |
| " :param input_tensor:\n", | |
| " :param filters:\n", | |
| " :param kernel_size:\n", | |
| " :param strides:\n", | |
| " :param is_conv_shortcuts: 直接连接或者投影连接\n", | |
| " :return:\n", | |
| " \"\"\"\n", | |
| " x = Conv2D_BN(input_tensor, filters, kernel_size, strides=strides, padding='same')\n", | |
| " x = Conv2D_BN(x, filters, kernel_size, padding='same')\n", | |
| " if is_conv_shortcuts:\n", | |
| " shortcut = Conv2D_BN(input_tensor, filters, kernel_size, strides=strides, padding='same')\n", | |
| " x = add([x, shortcut])\n", | |
| " else:\n", | |
| " x = add([x, input_tensor])\n", | |
| " return x\n", | |
| " \n", | |
| " \n", | |
| "def bottleneck_block(input_tensor, filters=(64, 64, 256), strides=(1, 1), is_conv_shortcuts=False):\n", | |
| " \"\"\"\n", | |
| " \n", | |
| " :param input_tensor:\n", | |
| " :param filters:\n", | |
| " :param strides:\n", | |
| " :param is_conv_shortcuts: 直接连接或者投影连接\n", | |
| " :return:\n", | |
| " \"\"\"\n", | |
| " filters_1, filters_2, filters_3 = filters\n", | |
| " x = Conv2D_BN(input_tensor, filters=filters_1, kernel_size=(1, 1), strides=strides, padding='same')\n", | |
| " x = Conv2D_BN(x, filters=filters_2, kernel_size=(3, 3))\n", | |
| " x = Conv2D_BN(x, filters=filters_3, kernel_size=(1, 1))\n", | |
| " if is_conv_shortcuts:\n", | |
| " short_cut = Conv2D_BN(input_tensor, filters=filters_3, kernel_size=(1, 1), strides=strides)\n", | |
| " x = add([x, short_cut])\n", | |
| " else:\n", | |
| " x = add([x, input_tensor])\n", | |
| " return x\n", | |
| " \n", | |
| " \n", | |
| "def ResNet34(input_shape=(224, 224, 3), n_classes=1000):\n", | |
| " \"\"\"\n", | |
| " \n", | |
| " :param input_shape:\n", | |
| " :param n_classes:\n", | |
| " :return:\n", | |
| " \"\"\"\n", | |
| " \n", | |
| " input_layer = Input(shape=input_shape)\n", | |
| " x = ZeroPadding2D((3, 3))(input_layer)\n", | |
| " # block1\n", | |
| " x = Conv2D_BN(x, filters=64, kernel_size=(7, 7), strides=(2, 2), padding='valid')\n", | |
| " x = MaxPooling2D(pool_size=(3, 3), strides=2, padding='same')(x)\n", | |
| " # block2\n", | |
| " x = identity_block(x, filters=64, kernel_size=(3, 3))\n", | |
| " x = identity_block(x, filters=64, kernel_size=(3, 3))\n", | |
| " x = identity_block(x, filters=64, kernel_size=(3, 3))\n", | |
| " # block3\n", | |
| " x = identity_block(x, filters=128, kernel_size=(3, 3), strides=(2, 2), is_conv_shortcuts=True)\n", | |
| " x = identity_block(x, filters=128, kernel_size=(3, 3))\n", | |
| " x = identity_block(x, filters=128, kernel_size=(3, 3))\n", | |
| " x = identity_block(x, filters=128, kernel_size=(3, 3))\n", | |
| " # block4\n", | |
| " x = identity_block(x, filters=256, kernel_size=(3, 3), strides=(2, 2), is_conv_shortcuts=True)\n", | |
| " x = identity_block(x, filters=256, kernel_size=(3, 3))\n", | |
| " x = identity_block(x, filters=256, kernel_size=(3, 3))\n", | |
| " x = identity_block(x, filters=256, kernel_size=(3, 3))\n", | |
| " x = identity_block(x, filters=256, kernel_size=(3, 3))\n", | |
| " x = identity_block(x, filters=256, kernel_size=(3, 3))\n", | |
| " # block5\n", | |
| " x = identity_block(x, filters=512, kernel_size=(3, 3), strides=(2, 2), is_conv_shortcuts=True)\n", | |
| " x = identity_block(x, filters=512, kernel_size=(3, 3))\n", | |
| " x = identity_block(x, filters=512, kernel_size=(3, 3))\n", | |
| " x = AveragePooling2D(pool_size=(7, 7))(x)\n", | |
| " x = Flatten()(x)\n", | |
| " x = Dense(n_classes, activation='softmax')(x)\n", | |
| " \n", | |
| " model = Model(inputs=input_layer, outputs=x)\n", | |
| " return model\n", | |
| " \n", | |
| " \n", | |
| "def ResNet50(input_shape=(224, 224, 3), n_classes=1000):\n", | |
| " \"\"\"\n", | |
| " \n", | |
| " :param input_shape:\n", | |
| " :param n_classes:\n", | |
| " :return:\n", | |
| " \"\"\"\n", | |
| " input_layer = Input(shape=input_shape)\n", | |
| " x = ZeroPadding2D((3, 3))(input_layer)\n", | |
| " # block1\n", | |
| " x = Conv2D_BN(x, filters=64, kernel_size=(7, 7), strides=(2, 2), padding='valid')\n", | |
| " x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)\n", | |
| " \n", | |
| " # block2\n", | |
| " x = bottleneck_block(x, filters=(64, 64, 256), strides=(1, 1), is_conv_shortcuts=True)\n", | |
| " x = bottleneck_block(x, filters=(64, 64, 256))\n", | |
| " x = bottleneck_block(x, filters=(64, 64, 256))\n", | |
| " # block3\n", | |
| " x = bottleneck_block(x, filters=(128, 128, 512), strides=(2, 2), is_conv_shortcuts=True)\n", | |
| " x = bottleneck_block(x, filters=(128, 128, 512))\n", | |
| " x = bottleneck_block(x, filters=(128, 128, 512))\n", | |
| " x = bottleneck_block(x, filters=(128, 128, 512))\n", | |
| " # block4\n", | |
| " x = bottleneck_block(x, filters=(256, 256, 1024), strides=(2, 2), is_conv_shortcuts=True)\n", | |
| " x = bottleneck_block(x, filters=(256, 256, 1024))\n", | |
| " x = bottleneck_block(x, filters=(256, 256, 1024))\n", | |
| " x = bottleneck_block(x, filters=(256, 256, 1024))\n", | |
| " x = bottleneck_block(x, filters=(256, 256, 1024))\n", | |
| " x = bottleneck_block(x, filters=(256, 256, 1024))\n", | |
| " # block5\n", | |
| " x = bottleneck_block(x, filters=(512, 512, 2048), strides=(2, 2), is_conv_shortcuts=True)\n", | |
| " x = bottleneck_block(x, filters=(512, 512, 2048))\n", | |
| " x = bottleneck_block(x, filters=(512, 512, 2048))\n", | |
| " x = AveragePooling2D(pool_size=(7, 7))(x)\n", | |
| " x = Flatten()(x)\n", | |
| " x = Dense(n_classes, activation='softmax')(x)\n", | |
| " \n", | |
| " model = Model(inputs=input_layer, outputs=x)\n", | |
| " return model\n", | |
| " \n", | |
| "def ResNet50_32(input_shape=(32, 32, 3), n_classes=1000):\n", | |
| " \"\"\"\n", | |
| " \n", | |
| " :param input_shape:\n", | |
| " :param n_classes:\n", | |
| " :return:\n", | |
| " \"\"\"\n", | |
| "# input_layer = Input(shape=input_shape)\n", | |
| " input_layer = Input(shape=(32,32,3))\n", | |
| " x = keras.layers.Lambda(lambda img: K.resize_images(img, 7, 7, data_format='channels_last') ,input_shape=(32, 32, 3))(input_layer)\n", | |
| " x = ZeroPadding2D(padding=3)(x)\n", | |
| " # block1\n", | |
| " x = Conv2D_BN(x, filters=64, kernel_size=(7, 7), strides=(2, 2), padding='valid')\n", | |
| " x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)\n", | |
| "\n", | |
| " # block2\n", | |
| " x = bottleneck_block(x, filters=(64, 64, 256), strides=(1, 1), is_conv_shortcuts=True)\n", | |
| " x = bottleneck_block(x, filters=(64, 64, 256))\n", | |
| " x = bottleneck_block(x, filters=(64, 64, 256))\n", | |
| " # block3\n", | |
| " x = bottleneck_block(x, filters=(128, 128, 512), strides=(2, 2), is_conv_shortcuts=True)\n", | |
| " x = bottleneck_block(x, filters=(128, 128, 512))\n", | |
| " x = bottleneck_block(x, filters=(128, 128, 512))\n", | |
| " x = bottleneck_block(x, filters=(128, 128, 512))\n", | |
| " # block4\n", | |
| " x = bottleneck_block(x, filters=(256, 256, 1024), strides=(2, 2), is_conv_shortcuts=True)\n", | |
| " x = bottleneck_block(x, filters=(256, 256, 1024))\n", | |
| " x = bottleneck_block(x, filters=(256, 256, 1024))\n", | |
| " x = bottleneck_block(x, filters=(256, 256, 1024))\n", | |
| " x = bottleneck_block(x, filters=(256, 256, 1024))\n", | |
| " x = bottleneck_block(x, filters=(256, 256, 1024))\n", | |
| " # block5\n", | |
| " x = bottleneck_block(x, filters=(512, 512, 2048), strides=(2, 2), is_conv_shortcuts=True)\n", | |
| " x = bottleneck_block(x, filters=(512, 512, 2048))\n", | |
| " x = bottleneck_block(x, filters=(512, 512, 2048))\n", | |
| " x = AveragePooling2D(pool_size=(7, 7))(x)\n", | |
| " x = Flatten()(x)\n", | |
| " x = Dense(n_classes, activation='softmax')(x)\n", | |
| "\n", | |
| " model = Model(inputs=input_layer, outputs=x)\n", | |
| " return model\n", | |
| "# 生成训练数据\n", | |
| " \n", | |
| "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", | |
| " \n", | |
| "img_size = (32,32)\n", | |
| "train_gen = ImageDataGenerator( validation_split=0.2)\n", | |
| "\n", | |
| "(x_train_1, y_train), (x_test_1, y_test) = load_data()\n", | |
| "\n", | |
| "import scipy\n", | |
| "import scipy.misc\n", | |
| "from skimage.transform import resize\n", | |
| "new_shape = (244,244,3)\n", | |
| "\n", | |
| "print(\"Generating Data\")\n", | |
| "\n", | |
| "#x_train = np.empty(shape=(x_train_1.shape[0],)+new_shape)\n", | |
| "#for idx in range(x_train_1.shape[0]):\n", | |
| "# x_train[idx] = resize(x_train_1[idx], new_shape)\n", | |
| "# if idx%100==0:\n", | |
| "# print(idx,\"/\",x_train_1.shape[0])\n", | |
| "x_train = x_train_1.astype(np.float32)\n", | |
| "x_train = x_train / 255\n", | |
| "#x_test = np.empty(shape=(x_test_1.shape[0],)+new_shape)\n", | |
| "#for idx in range(x_test_1.shape[0]):\n", | |
| "# x_test[idx] = resize(x_test_1[idx], new_shape)\n", | |
| "\n", | |
| "train_generator = train_gen.flow(x=x_train,y=y_train,\n", | |
| " batch_size=32,\n", | |
| " subset='training',\n", | |
| " )\n", | |
| "valid_generator = train_gen.flow(x=x_train,y=y_train,\n", | |
| " batch_size=32,\n", | |
| " subset='validation'\n", | |
| " )\n", | |
| " \n", | |
| "# 编译模型\n", | |
| "resize = keras.layers.Lambda(lambda img: K.resize_images(img, 7, 7, data_format='channels_last'), input_shape=(32, 32, 3),dtype=np.float32)\n", | |
| "resnet34, resnet50_1 = ResNet34(n_classes=10), ResNet50(input_shape=(224,224,3),n_classes=10)\n", | |
| "opt=keras.optimizers.SGD(learning_rate=0.01,)\n", | |
| " \n", | |
| "predef_resnet50 = ResNet50_32(n_classes=10)\n", | |
| " \n", | |
| "reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto',factor=0.1)\n", | |
| "#resnet34.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n", | |
| "#resnet50.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n", | |
| "predef_resnet50.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n", | |
| " \n", | |
| "# 训练\n", | |
| " \n", | |
| "#istory_34 = resnet34.fit(train_generator, \n", | |
| " # steps_per_epoch=train_generator.n//train_generator.batch_size, \n", | |
| " # validation_data=valid_generator, \n", | |
| " # validation_steps=valid_generator.n//valid_generator.batch_size, \n", | |
| " # epochs=50, \n", | |
| " # )\n", | |
| "#save_model(resnet34,\"/content/drive/My Drive/resnet34.net\")\n", | |
| " \n", | |
| "predef_history_50 = predef_resnet50.fit(train_generator, \n", | |
| " steps_per_epoch=train_generator.n//train_generator.batch_size, \n", | |
| " validation_data=valid_generator, \n", | |
| " validation_steps=valid_generator.n//valid_generator.batch_size, \n", | |
| " epochs=100, \n", | |
| "# callbacks=[reduce_lr],\n", | |
| " )\n", | |
| " \n", | |
| "#save_model(resnet50,\"/content/drive/My Drive/resnet50.net\")\n", | |
| " \n", | |
| "#history34 = history_34\n", | |
| "predef_history50 = predef_history_50\n", | |
| " \n", | |
| "# 绘制结果曲线\n", | |
| " \n", | |
| "import matplotlib.pyplot as plt\n", | |
| " \n", | |
| "plt.figure(figsize=(16, 8))\n", | |
| "plt.subplot(1, 2, 1)\n", | |
| "#plt.plot(np.arange(len(history34.history['loss'])), history34.history['loss'], label='resnet34 train loss')\n", | |
| "plt.plot(np.arange(len(predef_history50.history['loss'])), predef_history50.history['loss'], label='predef_resnet50 train loss')\n", | |
| "plt.legend(loc=0)\n", | |
| " \n", | |
| "plt.subplot(1, 2, 2)\n", | |
| "#plt.plot(np.arange(len(history34.history['val_loss'])), history34.history['val_loss'], label='resnet34 valid loss')\n", | |
| "plt.plot(np.arange(len(predef_history50.history['val_loss'])), predef_history50.history['val_loss'], label='predef_resnet50 valid loss')\n", | |
| "plt.legend(loc=0)\n", | |
| "plt.savefig('/content/drive/My Drive/predef_loss50.png')\n", | |
| " \n", | |
| "plt.figure(figsize=(16, 8))\n", | |
| "plt.subplot(1, 2, 1)\n", | |
| "#plt.plot(np.arange(len(history34.history['accuracy'])), history34.history['accuracy'], label='resnet34 train acc')\n", | |
| "plt.plot(np.arange(len(predef_history50.history['accuracy'])), predef_history50.history['accuracy'], label='predef_resnet50 train acc')\n", | |
| "plt.legend(loc=0)\n", | |
| " \n", | |
| "plt.subplot(1, 2, 2)\n", | |
| "#plt.plot(np.arange(len(history34.history['val_accuracy'])), history34.history['val_accuracy'], label='resnet34 valid acc')\n", | |
| "plt.plot(np.arange(len(predef_history50.history['val_accuracy'])), predef_history50.history['val_accuracy'], label='predef_resnet50 valid acc')\n", | |
| "plt.legend(loc=0)\n", | |
| " \n", | |
| "plt.savefig('/content/drive/My Drive/predef_accuracy50.png')\n", | |
| " \n", | |
| "# 保存模型到云端硬盘" | |
| ], | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Generating Data\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "error", | |
| "ename": "NameError", | |
| "evalue": "ignored", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m<ipython-input-4-7e1fe51a651a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[0mreduce_lr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReduceLROnPlateau\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmonitor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'val_loss'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatience\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfactor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[0mresnet34\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'categorical_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 303\u001b[0;31m \u001b[0mresnet50\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'categorical_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 304\u001b[0m \u001b[0mpredef_resnet50\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'categorical_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;31mNameError\u001b[0m: name 'resnet50' is not defined" | |
| ] | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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